Rice Maturity Level Segmentation in Paddy Fields Based on UAV Aerial Imagery Using the YOLOv8 Algorithm - Ma'ruf, Amin; Fadjeri, Akhmad (Journal of Engineering, Electrical and Informatics, 2026)
Accurate identification of rice maturity is an important factor in determining the optimal harvest period and maintaining grain quality. Conventional field observations are often influenced by subjective judg-ment and may produce inconsistent results across different observers. This study proposes an auto-mated approach for rice maturity segmentation by integrating Unmanned Aerial Vehicle (UAV) imagery with the YOLOv8 deep-learning model. A dataset consisting of 682 aerial images was collected from paddy fields and categorized into three classes: unripe, ripe, and unhealthy rice. The images were an-notated using bounding boxes and divided into training, validation, and testing subsets. Model training was performed using YOLOv8n for 100 epochs with a batch size of 16. Performance evaluation em-ployed accuracy, precision, recall, and F1-score metrics derived from the confusion matrix. Experimental results showed that the proposed framework achieved an accuracy of up to 93%, demonstrating its capability to identify rice maturity conditions effectively. The findings suggest that UAV-based moni-toring combined with deep learning can support precision agriculture by providing a faster, more ob-jective, and scalable alternative to manual field assessment.